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旅游地生态效率测度的SBM-DEA模型及实证分析
引用本文:彭红松,章锦河,韩娅,汤国荣,张瑜.旅游地生态效率测度的SBM-DEA模型及实证分析[J].生态学报,2017,37(2):628-638.
作者姓名:彭红松  章锦河  韩娅  汤国荣  张瑜
作者单位:南京大学国土资源与旅游学系, 南京 210023,南京大学国土资源与旅游学系, 南京 210023,安徽师范大学国土资源与旅游学院, 芜湖 241003,南京大学国土资源与旅游学系, 南京 210023,南京大学国土资源与旅游学系, 南京 210023
基金项目:国家自然科学基金资助项目(41271161,40971301)
摘    要:旅游地是典型的人地关系相互作用的特殊区域,旅游地的生态效率研究是其制定与实施包容性、持续性发展政策与措施的基础。采用基于时间序列、包含非期望产出的SBM-DEA模型方法,构建旅游地生态效率测度模型及评价指标体系,以黄山风景区为例,利用1981—2014年的投入产出数据,测度旅游地复合系统的生态效率,分析其演化特征和阶段,并利用Tobit回归模型对其影响因素进行实证检验。结果表明:(1)34年来,黄山风景区旅游生态效率(综合效率)不断提升,且具较大发展潜力,在分解效率中,技术效率较高,规模效率次之,规模效率是决定综合效率的关键因素;(2)旅游生态效率的演化经历了初期低效、快速成长、成熟高效、下行风险四个阶段,不同阶段效率的特征不同,影响因素也存在差异;(3)旅游生态效率完成了由规模报酬递增向递减的过渡,资源要素的投入冗余已成为现阶段阻碍生态效率的进一步提高的关键因素;(4)旅游发展水平、产业结构和技术水平对生态效率产生显著的正向影响,投资水平产生显著的负向影响,以废弃物末端治理为表征的环保规制对生态效率的提升作用并不显著。文章最后提出,在山岳型风景区发展初期,应尽可能扩大资源要素投入规模,进入成熟阶段后,则转向逐渐控制投入规模,改善技术能力和资源配置能力,摒弃过度依靠资源消耗和环境污染的粗放式发展模式,走精细化、可持续的发展道路。

关 键 词:旅游生态效率  时间序列SBM-DEA模型  Tobit回归分析  黄山风景区
收稿时间:2015/7/31 0:00:00
修稿时间:2016/5/10 0:00:00

Measurement and empirical analysis of eco-efficiency in tourism destinations based on a Slack-based Measure-Data Envelopment Analysis model
PENG Hongsong,ZHANG Jinhe,HAN Y,TANG Guorong and ZHANG Yu.Measurement and empirical analysis of eco-efficiency in tourism destinations based on a Slack-based Measure-Data Envelopment Analysis model[J].Acta Ecologica Sinica,2017,37(2):628-638.
Authors:PENG Hongsong  ZHANG Jinhe  HAN Y  TANG Guorong and ZHANG Yu
Institution:Department of Land Resources and Tourism Science, Nanjing University, Nanjing 210023, China,Department of Land Resources and Tourism Science, Nanjing University, Nanjing 210023, China,College of Territorial Resources and Tourism, Anhui Normal University, Wuhu 241003, China,Department of Land Resources and Tourism Science, Nanjing University, Nanjing 210023, China and Department of Land Resources and Tourism Science, Nanjing University, Nanjing 210023, China
Abstract:Many tourism destinations are characterized by interactions between people and the environment. Typically, research on the eco-efficiency of tourism destinations is the basis for the formulation and implementation of inclusive, sustainable development policies and measures. Extensive literature is available on the eco-efficiency evaluations of the environmental impact of human material production behavior, such as industry and manufacturing, but studies exploring tourism from the perspective of human consumption behavior are limited. Little systematic research has been conducted to investigate the eco-efficiency theoretical system and the calculation methods involved, with more attention paid to large-and medium-scale regional or urban cases. Small regional-scale cases, especially tourist destinations, are yet to be explored. Based on a time series Slack-based Measure-Data Envelopment Analysis (SBM-DEA) model, including unexpected output, we built a model to measure the eco-efficiency of tourism destinations and an evaluation index system. We selected the average wage level, new fixed asset investments, energy consumption, water consumption, and catering biological resource consumption as input indicators. Per capita tourism income was selected as the expected output indicator, and the emission indicators of tourism waste, namely, the amount of garbage, sewage, and waste gas emissions, were used to characterize the unexpected output indicators. We chose the Huangshan scenic area as an example and used the input and output data from 1981 to 2014 to measure the eco-efficiency of the tourist destination composite system and analyzed its evolution characteristics and phases. We used a Tobit regression model to empirically test the influencing factors. First, we explored the characteristics and the evolution of the eco-efficiency of the tourism destination; next, we distinguished the key factors that influenced this eco-efficiency and investigated the relationships between tourism eco-efficiency, tourism investment, and output factors. The following results were obtained:(1) In the past 34 years, eco-efficiency (technical efficiency) has grown continually in the Huangshan scenic area, which has a great development potential. Pure technical efficiency is the most influential, followed by scale efficiency, for decomposition. Scale efficiency is a decisive factor for technical efficiency. (2) The evolution of tourism ecological efficiency has four stages:initial inefficient stage, rapid growth stage, mature efficient stage, and downside risk stage. The eco-efficiency characteristics and influencing factors in different stages are different. (3) Tourism eco-efficiency is complete when scale transition returns from an increase to a decrease. Thus, the input redundancy of resources becomes the key factor preventing ecological efficiency from improving further in the present stage. (4) The level of tourism development, industrial structure, and technical level have a significant positive impact on eco-efficiency, but investment levels have a significant negative impact. The environmental regulation that emphasizes the management of waste is not effective in promoting eco-efficiency. This study proposed that the scale of resource inputs should be expanded as far as possible at the beginning of the mountain-type scenic area development. When the destination enters the mature stage, the investment scale should gradually be controlled. This involves improved technology and resource allocation, abandoning the extensive development pattern that results in overdependence on resource consumption and environmental pollution. The study contributes to related research perspectives and methods and promotes the sustainable development of tourism destinations.
Keywords:tourism eco-efficiency  time series of SBM-DEA model  Tobit regression analysis  Huangshan scenic area
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